Question

在Groupby文档中，我只看到应用于轴0索引的函数或列标签的分组示例。我没有看到讨论如何按照将一个函数应用于列而得到的标签进行分组的示例。我认为这将使用apply完成。以下示例是最好的方法吗？

df = pd.DataFrame({'name' : np.random.choice(['a','b','c','d','e'], 20), 
               'num1': np.random.randint(low = 30, high=100, size=20),
               'num2': np.random.randint(low = -3, high=9, size=20)})

df.head()

  name  num1 num2
0   d   34  7
1   b   49  6
2   a   51  -1
3   d   79  8
4   e   72  5

def num1_greater_than_60(number_num1):
    if number_num1 >= 60:
        return 'greater'
    else:
        return 'less'

df.groupby(df['num1'].apply(num1_greater_than_60))

Answer 1

来自DataFrame.groupby（）docs：

by : mapping, function, str, or iterable
    Used to determine the groups for the groupby.
    If ``by`` is a function, it's called on each value of the object's
    index. If a dict or Series is passed, the Series or dict VALUES
    will be used to determine the groups (the Series' values are first
    aligned; see ``.align()`` method). If an ndarray is passed, the
    values are used as-is determine the groups. A str or list of strs
    may be passed to group by the columns in ``self``

所以我们可以这样做：

In [35]: df.set_index('num1').groupby(num1_greater_than_60)[['name']].count()
Out[35]:
         name
greater    15
less        5

Answer 2

你可以不在这里申请

df.groupby(df.num1.gt(60))

df.num1.gt(60)
Out[774]: 
0      True
1      True
2      True
3      True
4     False
5      True
6      True
7      True
8     False
9      True
10    False
11     True
12     True
13     True
14    False
15     True
16    False
17    False
18     True
19    False
Name: num1, dtype: bool

Answer 3

一般情况下，我会通过创建派生列然后groupby来实现这一点 - 我发现这更容易跟踪，并且总是可以删除它或仅选择最后需要的列。

df = pd.DataFrame({'name' : np.random.choice(['a','b','c','d','e'], 20), 
               'num1': np.random.randint(low = 30, high=100, size=20),
               'num2': np.random.randint(low = -3, high=9, size=20)})

df['num1_greater_than_60'] = df['num1'].gt(60).replace(
    to_replace=[True, False], 
    value=['greater', 'less'])

df.groupby('num1_greater_than_60').dosomething()

pandas group by函数应用于列

3 个答案: